Effective model complexity is a relatively new, promising and useful problem in deep learning.
Detecting effective model complexity during training helps to investigate the usefulness of optimization algorithms [1]}, the role of regularizations [2]}, [3]}, and generalization capability [4]}, [5]}.
Furthermore, effective model complexity can be used to describe model compression ratio, since effective model complexity can be considered as a reflection of the information volume in the model [6]}.
Effective complexity can also be used for model selection and design to balance resource utilization and model performance.
